Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing

Abstract

Metal additive manufacturing (MAM) has advanced significantly, yet accurately predicting clad characteristics from processing parameters remains challenging due to process complexity and data scarcity. This study introduces a novel hybrid machine learning (ML) framework that integrates validated multi-physics computational fluid dynamics simulations with experimental data, enabling prediction of clad characteristics unattainable through conventional methods alone. Our approach uniquely incorporates physics-aware features, such as volumetric energy density and linear mass density, enhancing process understanding and model transferability. We comprehensively benchmark ML models across traditional, ensemble, and neural network categories, analyzing their computational complexity through Big O notation and evaluating both classification and regression performance in predicting clad geometries and process maps. The framework demonstrates superior prediction accuracy with sub-second inference latency, overcoming limitations of purely experimental or simulation-based methods. The trained models generate processing maps with 0.95 AUC (Area Under Curve) accuracy that directly guide MAM parameter selection, bridging the gap between theoretical modeling and practical process control. By integrating physics-based simulations with ML techniques and physics-aware features, our approach achieves an R2 of 0.985 for clad geometry prediction and improved generalization over traditional methods, establishing a new standard for MAM process modeling. This research advances both theoretical understanding and practical implementation of MAM processes through a comprehensive, physics-aware machine learning approach.